import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt

import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib

For this excercise, we have written the following code to load the stock dataset built into plotly express.

stocks = px.data.stocks()
stocks.head()
"""ewa"""

'ewa'

Question 1:

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

# YOUR CODE HERE
fig, ax = plt.subplots()
ax.plot(stocks.date, stocks.GOOG)
ax.xaxis.set_major_locator(plt.MaxNLocator(5))
fig.show()

/var/folders/90/9v48d7qs3mbfrbykf9mxnwqh0000gn/T/ipykernel_10365/1666324507.py:5: UserWarning:

Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.

Question 2:

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

# YOUR CODE HERE
fig, ax = plt.subplots()

#for st in stocks.columns:
#    if not st == "date":
#        ax.plot(stocks.date, stocks[st], label=st)
ax.plot(stocks.date, stocks.GOOG,label="GOOG", linestyle='dashdot', color='r')
ax.plot(stocks.date, stocks.AAPL,label="AAPL", linestyle='solid', color='b')
ax.plot(stocks.date, stocks.AMZN,label="AMZN", linestyle='dotted', color='y')
ax.plot(stocks.date, stocks.FB,label="FB", linestyle='dashed', color='g')
ax.plot(stocks.date, stocks.MSFT,label="MSFT")

ax.xaxis.set_major_locator(plt.MaxNLocator(5))
ax.legend()
fig.show()

/var/folders/90/9v48d7qs3mbfrbykf9mxnwqh0000gn/T/ipykernel_10365/2566576305.py:15: UserWarning:

Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.

Seaborn

First, load the tips dataset

tips = sns.load_dataset('tips')
tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Question 3:

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

• Are there differences between male and female when it comes to giving tips?
• What attribute correlate the most with tip?

# YOUR CODE HERE
#Question: on which day is the relatively highest tip given?

tips['relative'] = tips.tip/tips.total_bill
sns.boxplot(data=tips, x="day", y="relative")

<AxesSubplot:xlabel='day', ylabel='relative'>

Plotly Express

Question 4:

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset

Hints:

• Turn stocks dataframe into a structure that can be picked up easily with plotly express

allstocks = ["AAPL", "AMZN", "FB"]

fig.data[0].line.color = 'rgb(204, 20, 204)'

fig = px.line(stocks, x='date', y=allstocks)

fig.show()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [8], in <cell line: 3>()
      1 allstocks = ["AAPL", "AMZN", "FB"]
----> 3 fig.data[0].line.color = 'rgb(204, 20, 204)'
      5 fig = px.line(stocks, x='date', y=allstocks)
      7 fig.show()

AttributeError: 'Figure' object has no attribute 'data'

The tips dataset

#Question: what is the average bill custumers get when eating out?

tipsdataset = px.data.tips()
fig = px.box(tipsdataset, y="total_bill")
fig.show()

Question 5:

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

• Extract the 2007 year data from the dataframe. You have to process the data accordingly
• use plotly bar
• Add different colors for different continents
• Sort the order of the continent for the visualisation. Use axis layout setting
• Add text to each bar that represents the population

#load data
df = px.data.gapminder()
df.head()

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4

# YOUR CODE HERE

year2007 = df.query("year == 2007")
fig = px.bar(year2007, x='continent', y='pop', color='continent', text='continent')

fig.update_xaxes(categoryorder='total descending')

fig.show()